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Among those diseases threatening human health and well-being, many epidemic and infectious diseases are closely related to natural environment due to the presence, breeding and evolution of their pathogens or reservoir hosts, especially vector-borne diseases (e.g. schistosomiasis, malaria and dengue, etc.) which rely heavily on their vectors. Therefore, monitoring the diseases' vector is an important way to prevent and control the vector-borne diseases.Because of complex spatial distribution and dispersion of typical diseases and their vectors, it is difficult to acquire relevant environmental factor data by traditional in-situ measurements. Remote sensing technology provides the capability of obtaining temporal-spatial variations of ground environmental factors. However, remote sensing experts may not exactly know what environmental factors are required to identify the incubators of vector-borne diseases. On the other hand, effective RS data processing and parameters retrieval techniques are also challenges for hygiene experts who are lack of experience of remote sensing applications. Taking into account of different type of massive data are involved, computing scientists with substantial intelligent data analysis expertise is crucial to successfully incorporate advance intelligent data analysis, such as data mining, pattern analysis. Consequently, any single of these disciplines is insufficient, it is essential to bring together scientists from computing science and remote sensing along with domain experts to foster a substantial collaboration.This proposed project aims to apply advanced remote sensing and computing technologies into monitoring and early warning of vector-borne diseases, e.g. schistosomiasis, malaria and dengue. First is to reveal environmental factors which have significant influences on the breeding of epidemic disease and its vectors. Then the project will make full use of the advantage of European and Chinese earth observation resources and the partners capability to develop parameter inversion, feature extraction and pattern analysis methods that will be used to characterise environmental features and habitants that are mostly suitable for the growth and dispersion of vector-borne disease and dynamic monitoring. Furthermore, temporal-spatial models of the distribution of vector-borne diseases will be developed by data mining techniques. Finally, the driving mechanism and data assimilation methods of land surface process model will be explored in order to implement identification and early warning of vector-borne disease transmission areas. All the institution of the project can provide sufficient funding to run the whole project successfully. The outcomes of the project will help to decrease the scope and extent of vector-borne diseases, and improve prevention & control capabilities to vector-borne diseases. Additionally, the research results can be used to assess environmental characteristics around the sits of major infrastructure and facilities, and provide the suggestion on site selection and implementation of infrastructures. The synthetic feature extraction techniques developed for multi-source multi-level remote sensing data can also be applied to other service fields, sustainably making contribution to knowledge within the communities.

Approximately half of the world’s population is at the risk of at least one vector-borne parasitic disease. The survival of intermediate hosts of vector-borne parasitic diseases is governed by various environmental factors, and remote sensing can be used to characterize and monitor environmental factors related to intermediate host breeding and reproduction, and become a powerful means to monitor the vector-borne parasitic diseases. In this research, satellite remotely sensed data has been used to obtain the environmental factors (vegetation, soil,temperature, terrain et al.), which are related to the living, multiplying and transmission of intermediate host. Then based on ground truth data, the remote sensing monitoring model of the intermediate host has been developed, which can enhance the remote sensing monitoring capabilities of the vector-borne parasitic disease and provide the theoretical foundation and technical support for diseases prevention and control.